docs: def _load_csv(self, file_name, folder_name, head_row, index_col, convert_col, concat_files):
          """ Load single csv file.
  
          Parameters
          ----------
          file_name       : str
              CSV file to be imported. Defaults to '*' - all csv files in the folder.
          folder_name     : str
              Folder where file resides. Defaults to '.' - current directory.
          head_row        : int
              Skips all rows from 0 to head_row-1
          index_col       : int
              Skips all columns from 0 to index_col-1
          convert_col     : bool
              Convert columns to numeric type
          concat_files    : bool
              Appends data from files to result dataframe
  
          Returns
          -------
          pd.DataFrame()
              Dataframe containing csv data
  
          """
  
          # Denotes all csv files
          if file_name == "*":
  
              if not os.path.isdir(folder_name):
                  raise OSError('Folder does not exist.')
              else:
                  file_name_list = sorted(glob.glob(folder_name + '*.csv'))
  
                  if not file_name_list:
                      raise OSError('Either the folder does not contain any csv files or invalid folder provided.')
                  else:
                      # Call previous function again with parameters changed (file_name=file_name_list, folder_name=None)
                      # Done to reduce redundancy of code
                      self.import_csv(file_name=file_name_list, head_row=head_row, index_col=index_col,
                                      convert_col=convert_col, concat_files=concat_files)
                      return self.data
  
          else:
              if not os.path.isdir(folder_name):
                  raise OSError('Folder does not exist.')
              else:
                  path = os.path.join(folder_name, file_name)
  
                  if head_row > 0:
                      data = pd.read_csv(path, index_col=index_col, skiprows=[i for i in range(head_row-1)])
                  else:
                      data = pd.read_csv(path, index_col=index_col)
  
                  # Convert time into datetime format
                  try:
                      # Special case format 1/4/14 21:30
                      data.index = pd.to_datetime(data.index, format='%m/%d/%y %H:%M')
                  except:
                      data.index = pd.to_datetime(data.index, dayfirst=False, infer_datetime_format=True)
  
          # Convert all columns to numeric type
          if convert_col:
              # Check columns in dataframe to see if they are numeric
              for col in data.columns:
                  # If particular column is not numeric, then convert to numeric type
                  if data[col].dtype != np.number:
                      data[col] = pd.to_numeric(data[col], errors="coerce")
  
          return data